ABSTAT: Ontology-Driven Linked Data Summaries with Pattern Minimalization

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• 关键词：Data summarization ; Knowledge patterns ; Linked data
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9989
• 期：1
• 页码：381-395
• 全文大小：1,330 KB
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• 作者单位：Blerina Spahiu (19)
  Riccardo Porrini (19)
  Matteo Palmonari (19)
  Anisa Rula (19)
  Andrea Maurino (19)
  
  19. University of Milano-Bicocca, Milan, Italy
  
• 丛书名：The Semantic Web
• ISBN：978-3-319-47602-5
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9989

文摘

An increasing number of research and industrial initiatives have focused on publishing Linked Open Data, but little attention has been provided to help consumers to better understand existing data sets. In this paper we discuss how an ontology-driven data abstraction model supports the extraction and the representation of summaries of linked data sets. The proposed summarization model is the backbone of the ABSTAT framework, that aims at helping users understanding big and complex linked data sets. The proposed model produces a summary that is correct and complete with respect to the assertions of the data set and whose size scales well with respect to the ontology and data size. Our framework is evaluated by showing that it is capable of unveiling information that is not explicitly represented in underspecified ontologies and that is valuable to users, e.g., helping them in the formulation of SPARQL queries.